Gravitational Models Explain Shifts on Human Visual Attention

• URL: http://arxiv.org/abs/2009.06963v1
• Date: Tue, 15 Sep 2020 10:12:41 GMT
• Title: Gravitational Models Explain Shifts on Human Visual Attention
• Authors: Dario Zanca, Marco Gori, Stefano Melacci, Alessandra Rufa
• Abstract summary: Visual attention refers to the human brain's ability to select relevant sensory information for preferential processing. Various methods to estimate saliency have been proposed in the last three decades. We propose a gravitational model (GRAV) to describe the attentional shifts.
• Score: 80.76475913429357
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual attention refers to the human brain's ability to select relevant sensory information for preferential processing, improving performance in visual and cognitive tasks. It proceeds in two phases. One in which visual feature maps are acquired and processed in parallel. Another where the information from these maps is merged in order to select a single location to be attended for further and more complex computations and reasoning. Its computational description is challenging, especially if the temporal dynamics of the process are taken into account. Numerous methods to estimate saliency have been proposed in the last three decades. They achieve almost perfect performance in estimating saliency at the pixel level, but the way they generate shifts in visual attention fully depends on winner-take-all (WTA) circuitry. WTA is implemented} by the biological hardware in order to select a location with maximum saliency, towards which to direct overt attention. In this paper we propose a gravitational model (GRAV) to describe the attentional shifts. Every single feature acts as an attractor and {the shifts are the result of the joint effects of the attractors. In the current framework, the assumption of a single, centralized saliency map is no longer necessary, though still plausible. Quantitative results on two large image datasets show that this model predicts shifts more accurately than winner-take-all.

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